Regulating algorithmic hiring practices will help move society closer to the national ideal of equal opportunity in spaces of employment, explains Ifeoma Ajunwa of University of North Carolina School of Law in an article for the Harvard Journal of Law & Technology.In this week’s Saturday Seminar, scholars propose methods to counter discrimination in algorithmic hiring. They raised concerns that more companies would use algorithms for hiring due to pandemic restrictions, which could reproduce and deepen systemic patterns of discrimination in the workforce. Ten senators wrote to the EEOC in December 2020, requesting information about the Commission’s authority to investigate companies that offer hiring technologies. Equal Employment Opportunity Commission (EEOC) issued guidelines in 1978 for how employers can and should choose their employees.
Other federal laws prohibit employment discrimination based on pregnancy, disability, age, and genetic information. Algorithms that analyze video interviews may struggle with facial recognition for darker-skinned applicants, as well as penalize non-native speakers or people with disabilities.įederal civil rights law prohibits employment discrimination based on race, color, religion, sex, and national origin. Programs that review resumes can lead to discrimination if resumes include a Black-sounding name, list a women’s college, or mention a disability. Algorithms can influence hiring decisions by targeting job postings based on factors, such as age or gender, which limits who can apply. The cost to fairness and equity, however, is unclear.Īs recruiters use algorithms at more steps of the hiring process, bias can enter hiring decisions in several ways. Although Amazon abandoned the tool in 2017 before deployment, it illustrates how algorithms can reproduce existing patterns of inequality.Īccording to a LinkedIn survey, 67 percent of recruiters and hiring managers believe that artificial intelligence helps save time. The algorithm replicated historical hiring patterns, discriminating against women applicants. Amazon sought to create a program to screen resumes for top talent, but it trained its algorithm using a decade of resumes from mostly male applicants. What if hiring algorithms rejected job applicants explicitly for being women? Amazon’s experimental hiring algorithm did just that in 2015.
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